Event Format Duration: 10:00 AM – 4:00 PM IST Format: In-person Registration: Open for registration till 14th Feb 2026 Final Attendance: Invite-only confirmation (based on relevance and capacity) Location: Will be shared only with confirmed participants Register here: https://forms.gle/4uHZNKxBH7xcCwEU6
Event Overview Generative AI is rapidly evolving from experimentation to enterprise adoption. However, building reliable, secure, and scalable GenAI solutions requires strong data foundations, governance, and architecture — not just access to large language models.
This full-day session focuses on how Databricks enables organisations to move from raw data to governed intelligence using Generative AI. The session is designed to be practical, architecture-driven, and grounded in real-world enterprise use cases.
While registration is open to all, final participation will be confirmed via invitation to ensure a focused and high-quality learning environment.
Who Should Attend This event is open to:
Data Engineers
Data Scientists
ML / AI Engineers
Analytics Engineers
BI professionals exploring GenAI
Final-year students (Data, AI, or Engineering backgrounds)
Note: This is not an introductory AI session. Participants are expected to have basic familiarity with data or analytics concepts.
Full-Day Agenda (10:00 AM – 4:00 PM)
10:00 – 10:20 | Welcome and Session Orientation
Event objectives and structure
How the day will progress
What participants should expect by the end of the session
10:20 – 11:00 | GenAI in the Enterprise: Reality vs Hype
Why GenAI initiatives fail without strong data foundations
Why ChatGPT-style demos don’t translate to enterprise success
Common challenges such as hallucinations, data leakage, and lack of governance
Why GenAI is a data engineering and ML problem, not just prompting
Where Databricks fits in the enterprise GenAI landscape
11:00 – 11:45 | Databricks GenAI Reference Architecture
Lakehouse architecture for GenAI workloads
Delta Lake as a reliable and auditable data layer
Feature engineering and data preparation for AI
Vector search and embedding workflows
Unity Catalog for governance and access control
MLflow for experiment tracking and model lifecycle
11:45 – 12:00 | Break
12:00 – 12:45 | Core GenAI Concepts and Design Patterns
How production-grade GenAI systems are built
LLMs (OpenAI, Azure OpenAI, and open-source models)
Embeddings and semantic similarity
Retrieval-Augmented Generation (RAG)
Prompt versioning, evaluation, and observability
Structured versus unstructured GenAI use cases
Real-world examples include:
Internal knowledge assistants
Compliance and policy search
GenAI over enterprise BI data
12:45 – 1:30 | Lunch Break
1:30 – 2:45 | Live Demo: Building GenAI on Databricks
Ingesting enterprise data into Delta Lake
Creating embeddings and vector indexes
Implementing a RAG pipeline
Querying data using an LLM
Tracking experiments with MLflow
Applying governance using Unity Catalog
Focus will be on architecture, data flow, and decision-making rather than UI demonstrations.
2:45 – 3:00 | Break
3:00 – 3:30 | Governance, Security and Cost Control
What makes GenAI enterprise-ready
Role-based access control for GenAI systems
Handling sensitive and regulated data
Monitoring, observability, and model drift
Cost optimisation strategies
Why Databricks is safer than ad-hoc GenAI stacks
3:30 – 3:50 | Career and Industry Mapping
GenAI roles, skills, and expectations
Data Engineer vs ML Engineer vs AI Engineer
Skill expectations for GenAI projects
Portfolio and project guidance
What not to over-focus on, such as prompt-only roles
3:50 – 4:00 | Q and A and Closing
Learning Outcomes Participants will:
Understand enterprise GenAI architecture on Databricks
Learn how Lakehouse and GenAI work together
Gain clarity on RAG, embeddings, vector search, and governance
See a real-world GenAI implementation end to end
Understand career pathways in GenAI
Prerequisites
Basic understanding of data pipelines or analytics
Familiarity with SQL or Python is beneficial
Willingness to think beyond GenAI demos
Registration and Selection Process
Registration is open to all
Participants must fill out the registration form
Final participation will be confirmed via email invitation
Seats are limited to maintain quality and interaction
Register here: https://forms.gle/4uHZNKxBH7xcCwEU6
